When information is transmitted over a communications link between a receiver and a transmitter, the bits that describe the information being transmitted can be corrupted. In other words, the receiver may determine that a received bit that is supposed to be a binary 1 is a binary 0, and vice versa. Corruption of bits in a data stream may be caused by a variety of factors or components in the communications link. For example, in an optical fiber network, optical energy is transmitted in the form of optical pulses that have particular levels that correspond to binary 1s and 0s. If the level of the optical energy is too low, the receiver can have difficulty determining whether a pulse corresponds to a binary 1 or a binary 0. Repeaters, or amplifiers, normally are disposed at particular locations along the communications link that amplify the optical signals so that they are at the proper levels to enable the receiver to determine whether it has received a binary 1 or a binary 0. Typically, the optical signals are converted into electrical signals at the repeaters. The electrical signals are then amplified and converted into optical signals, which are then modulated back onto the optical fiber. Similarly, at the receiver, the optical signals typically are converted back into electrical signals, which the receiver compares to a threshold value to determine whether it has received a binary 1 or a binary 0.
Because it is possible for the bits to be corrupted, techniques have been developed and implemented that provide error correction. In other words, if a bit received by the receiver is erroneously determined to be a binary 1 when it was meant to be a binary 0 when it was transmitted, and vice versa, receivers utilize various techniques to determine whether a bit value has been incorrectly identified and to correct the bit value. One known technique used for such purposes is generally referred to as the “Automatic Request To Transmit” (ARM) technique. In accordance with this technique, when the receiver detects a bit error, it sends a signal to the transmitter that tells the transmitter to retransmit the block of data that contained the error. The receiver processes the retransmitted data block and detects bit errors. The data block may need to be retransmitted several times before the receiver determines that the data is without error. Of course, retransmitting data utilizes bandwidth and generally slows down the overall throughput of the communications system.
A technique known as Forward Error Correction (FEC) is commonly used in the communications industry to reduce errors in data being transmitted over a communications link without requiring retransmission of data. FEC not only detects bit errors, but corrects detected bit errors. One of the primary advantages of FEC over ARM is that no retransmission of data is required with FEC. This is because FEC techniques introduce redundancy in the data bits that enables the receiver of a communications system to detect errors in data being transmitted and to correct the detected errors. The redundancy generally is introduced by utilizing data bits from the data stream to encode the data stream. The receiver has a decoder that has intelligence with regard to the encoding scheme used by the transmitter, which enables the receiver to decode the data and detect and correct errors without the need for retransmission. Another advantage of FEC is that, because it does not require retransmission of data, simplex links can be used, which is desirable in certain situations, such as when the receivers are receive-only terminals.
Generally, FEC techniques and systems fall into two broad categories, namely, block coding and convolution coding. Various block coding and convolution coding techniques are currently in use in the communications industry. In the past, the use of block codes has generally been limited to use in communications systems that have relatively low data rates for various reasons, such as the aforementioned adverse effects on overall coding gain (signal-to-noise ratio, Eb/No), which is expressed in decibels (dB), for short data bursts and the reduced overall throughput that can result from the synchronization requirement. Convolution coding has generally been the preferred FEC technique for high data rate implementations. However, convolution coding results in higher output bit error rates (BER) than the output (BERs) that can be achieved using block coding. Some customers want FEC systems with very low BERs (e.g., 10−15), which generally cannot be achieved using convolution coding, but which can be achieved using block coding, such as Bose-Chaudhuri-Hocquenghem (BCH) block coding, for example.
Error correction coding with algebraic codes such as BCH and Reed-Solomon (RS) codes, for example, requires arithmetic operations in finite fields or Galois Fields. BCH and RS codes of length 8191 or shortened versions of the code have arithmetic operations in Galois Field (GF) of, for example (213), which is a binary extension field with 8192 (213) elements. This corresponds to a polynomial of the order 13, which has 213 binary coefficients. Of course, polynomials of orders other than 13 may also be used. Because the multiplier is used extensively in the decoding of such codes, an efficient implementation of the multiplier would result in a much smaller implementation of the decoder. To date, Galois multipliers utilize an enormous number of logic gates to perform the logic operations that are necessary to operate on such large numbers of bits.
It would be desirable to provide a Galois multiplier that is capable of being implemented with a relatively small number of gates for a given binary extension field. Reducing the number of gates utilized would increase the decoding speed, the amount of real estate on the integrated circuit (IC) utilized by the decoder, and the amount of power consumed by the decoder.
Accordingly, a need exists for a method and apparatus for performing Galois multiplication operations with increased efficiency in terms of the number of gates needed to perform the operations, the amount of area on the IC required for implementation of the decoder, and the power consumption requirements associated with the Galois multiplier logic of the decoder.